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Full Description
Up-to-date reference enabling readers to address the full spectrum of AI security challenges while maintaining model utility
Generative AI Security: Defense, Threats, and Vulnerabilities delivers a technical framework for securing generative AI systems, building on established standards while focusing specifically on emerging threats to large language models and other generative AI systems. Moving beyond treating AI security as a dual-use technology, this book provides detailed technical analysis of three critical dimensions: implementing AI-powered security tools, defending against AI-enhanced attacks, and protecting AI systems from compromise through attacks like prompt injection, model poisoning, and data extraction.
The book provides concrete technical implementations supported by real-world case studies of actual AI system compromises, examining documented cases like the DeepSeek breaches, Llama vulnerabilities, and Google's CaMeL security defenses to demonstrate attack methodologies and defense strategies while emphasizing foundational security principles that remain relevant despite technological shifts. Each chapter progresses from theoretical foundations to practical applications.
The book also includes an implementation guide and hands-on exercises focusing on specific vulnerabilities in generative AI architectures, security control implementation, and compliance frameworks.
Generative AI Security: Defense, Threats, and Vulnerabilities discusses topics including:
Machine learning fundamentals, including supervised, unsupervised, and reinforcement learning and feature engineering and selection
Intelligent Security Information and Event Management (SIEM), covering AI-enhanced log analysis, predictive vulnerability assessment, and automated patch generation
Deepfakes and synthetic media, covering image and video manipulation, voice cloning, audio deepfakes, and AI's greater impact on information integrity
Security attacks on generative AI, including jailbreaking, adversarial, backdoor, and data poisoning attacks
Privacy-preserving AI techniques including federated learning and homomorphic encryption
Generative AI Security: Defense, Threats, and Vulnerabilities is an essential resource for cybersecurity professionals and architects, engineers, IT professionals, and organization leaders seeking integrated strategies that address the full spectrum of Generative AI security challenges while maintaining model utility.
Contents
About the Authors xi
Preface xiii
Introduction xv
1 Generative AI in Cybersecurity 1
1.1 What Is Generative AI? 1
1.2 The Evolution of AI in Cybersecurity 4
1.3 Overview of GAI in Security 5
1.4 Current Landscape of Generative AI Applications 8
1.5 A Triangular Approach 10
Quiz 19
References 21
2 Understanding Generative AI Technologies 25
2.1 ML Fundamentals 25
2.2 Deep Learning and Neural Networks 29
2.3 Generative Models 34
2.4 NLP in Generative AI 42
2.5 Computer Vision in Generative AI 44
2.6 Conclusion 47
Chapter 2 Quiz 52
References 54
3 Generative AI as a Security Tool 61
3.1 AI-Powered Threat Detection and Response 61
3.2 Automated Vulnerability Discovery and Patching 69
3.3 Intelligent SIEMs 73
3.4 AI in Malware Analysis and Classification 78
3.5 Generative AI in Red Teaming 85
3.6 J-Curve for Productivity in AI-Driven Security 90
3.7 Regulatory Technology (RegTech) 93
3.8 AI for Emotional Intelligence (EQ) in Cybersecurity 96
References 103
4 Weaponized Generative AI 111
4.1 Deepfakes and Synthetic Media 111
4.2 AI-Powered Social Engineering 117
4.3 Automated Hacking and Exploit Generation 123
4.4 Privacy Concerns 127
4.5 Weaponization of AI: Attack Vectors 132
4.6 Defensive Strategies Against Weaponized Generative AI 147
Weaponized AI Cybersecurity Quiz 159
References 161
5 Generative AI Systems as a Target of Cyber Threats 171
5.1 Security Attacks on Generative AI 171
5.2 Privacy Attacks on Generative AI 192
5.3 Attacks on Availability 198
5.4 Physical Vulnerabilities 201
5.5 Model Extraction and Intellectual Property Risks 203
5.6 Model Poisoning and Supply Chain Risks 208
5.7 Open-Source GAI Models 211
5.8 Application-Specific Risks 215
5.9 Challenges in Mitigating Generative AI Risks 220
Quiz 226
References 228
6 Defending Against Generative AI Threats 241
6.1 Deepfake Detection Techniques 241
6.2 Adversarial Training and Robustness 244
6.3 Secure AI Development Practices 247
6.4 AI Model Security and Protection 252
6.5 Privacy-Preserving AI Techniques 257
6.6 Proactive Threat Intelligence and AI Incident Response 260
6.7 MLSecOps/SecMLOPs for Secure AI Development 263
Quiz: FinTech Solutions AI Defense Quiz 271
References 274
7 Ethical and Regulatory Considerations 283
7.1 Ethical Challenges in AI Security 283
7.2 AI Governance Frameworks 288
7.3 Current and Emerging AI Regulations 296
7.4 Responsible AI Development and Deployment 303
7.5 Balancing Innovation and Security 305
Ethical and Regulatory AI Security Quiz 315
References 318
8 Future Trends in Generative AI Security 323
8.1 Quantum Computing and AI Security 323
8.2 Human Collaboration in Cybersecurity 335
8.3 Advancements in XAI 340
8.4 The Role of Generative AI in Zero Trust 343
8.5 Micromodels 347
8.6 AI and Blockchain 349
8.7 Artificial General Intelligence (AGI) 351
8.8 Digital Twins 355
8.9 Agentic AI 357
8.10 Multimodal Models 363
8.11 Robotics 366
Triangular Framework for Generative AI Security Quiz 373
References 376
9 Implementing Generative AI Security in Organizations 385
9.1 Assessing Organizational Readiness 386
9.2 Developing an AI Security Strategy 389
9.3 Shadow AI 393
9.4 Building and Training AI Security Teams 396
9.5 Policy Recommendations for AI and Generative AI Implementation: A Triangular Approach 400
9.5.1 AI as a Tool: Leveraging Capabilities Responsibly 400
9.5.2 AI as a Weapon: Mitigating Malicious Use 401
9.5.3 AI as a Target: Protecting AI Systems 401
9.5.4 Long-Term Strategic Considerations 402
9.5.5 A Triangular Path Forward 402
CyberSecure AI Security Implementation Quiz 408
References 410
10 Future Outlook on AI and Cybersecurity 413
10.1 The Evolving Role of Security Professionals 413
10.2 AI-Driven Incident Response and Recovery 414
10.3 GAI Security Triad Framework (GSTF) 417
10.3.1 GAI Security Triad Framework (GSTF) Implementation Guide 420
10.3.2 Prerequisites 420
10.3.2.1 Inventory of GAI Systems and Applications 420
10.3.2.2 Access to System Documentation and Architecture Diagrams 421
10.3.2.3 Security Team Engagement 421
10.3.2.4 Stakeholder Buy-In Across Development and Operations 421
10.3.2.5 Basic Understanding of AI/ML Security Concepts 421
10.3.3 Framework Dimensions Implementation 422
10.3.4 Methodology Flow Implementation 432
10.4 Preparing for Future Challenges 441
10.5 Responsible AI Security 444
Practice Quiz: AI Security Triangular Framework 446
References 453
Index 455
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